How eight levels of manufacturing employees can use data to change their future

The right program implemented the right way can be a game changer for your business. 

Imagine trading intuition and reactive business decisions for data-driven, proactive decisions. Imagine a team having constructive conversations focused on solutions instead of emotional finger-pointing …. 

Follow us on this journey to see how Wipfli consultants can help a manufacturer implement Machine Metrics and extract data that can be embraced by employees at all levels to rewrite their future. 

CEO

Who she is: Second-generation leader who is excellent at sourcing new business and listening to her team.

Business objective: Wants 30,000-foot view of operations to create sustainable growth.

• Capacity constraints
• Investment requirements
• Profitability stratification

• Used overall equipment effectiveness data to measure availability, performance and quality.
• Identified planned downtimes as key area for improvement.
• Did a deeper dig that showed shift changeovers, lack of personnel could be key to improving capacity.
• Could handle a year’s growth if hired skilled personnel.
• Launched “Make every minute count. Machines are ready, how about you?” campaign with team competition.

CFO

Who he is:The CFO who wants to help others in the company learn their cost of goods, and increase margins so they can make proactive decisions.

Business objective: Maintaining gross margins.

• Reducing operational variances
• Improving job profitability
• Decreasing cost of goods sold

• Pulled production reports to analyze what jobs are they manufacturing efficiently. Which are inefficient, contributing to higher cost of goods?
• Analyzed data to see if higher cost of goods was due to poor pricing or poor operations.
• Determined operations needed to improve, especially in area of unplanned downtime.
• Had technicians, maintenance and quality inspectors sit together and have scrum sessions beginning and end to shift. Collaborating, they became more proactive and were able to reduce unplanned downtime. 

Sales and quoting

Who she is: Master of spread sheets that maintains her own reference guide because she doesn’t trust the ERP system. She wants to be more than efficient at her job. She also wants to be more effective. 

Business objective: Bidding on and winning profitable jobs.

• Increasing product complexity
• Ambiguity of previous job performance
• Inability to trust manufacturing standards

• Pulled data on past jobs to make a dynamic reference guide for decision making and quoting.
• To get more realistic estimates, she worked with floor supervisors to dig deeper into least profitable jobs.
• Saw that uncategorized time was key differentiator in profitability.
• Identified that uncategorized time was due to operator set ups and was able to account for that in future quotes.
• She used the information with the existing customer, bringing new perspective and appreciation. And won additional business. 

Manufacturing lead

Who he is: The guy who always gets the job done no matter what. His success has been built on a reactive culture of putting out fires. But he wants to change that culture to help the company hit that $50 million goal. 

Business objective: Operating an efficient and cost-effective plant using data, not instincts.

• Visibility of real-time plant performance
• Data capture of completed jobs - debrief
• Data driven manufacturing patterns
• Bidding on and winning profitable jobs

• Reviewed data to see what numbers were important for him to see so he could drive proactive decisions and post-job learnings.
• Set up dashboard to review:
  • Current status of machines 
  • Current progress for the day and order
  • How far ahead or behind jobs were 
  • Immediate incident reports for each shift
  • Reasons why
• Found out that efficiency was eroding during shift startups, staff assignments and break transitions.
• Keeps his dashboard open on his tablet as he moves around shop floor and has immediate, proactive conversations.

Planning and scheduling

Who she is: She holds a lot of historical knowledge in her head but spends most of her day chasing current information she can make “right now” decisions. 

Business objective: Scheduling jobs through profitable work centers.

• Improving manufacturing lead times and increasing throughput
• Optimizing highest-performing work centers
• She needs to find patterns to predict performance

• Picked up dashboard from Manufacturing lead.
• Established a watch list that helped the team schedule work centers and have proactive discussions with supervisors.
• Analyzed the best and worst performing work centers.
• Analyzed which centers have unpredictability.
• Used data to review previous jobs and efficiency results to plan future jobs.
• Used data to determine when results fit a pattern or an outlier.

Engineer

Who he is: He hears a lot of frustrations from people working with equipment and managers unhappy with results. He has to sift through those comments and complaints to see if the problem is related to equipment versus people or process so he can come up with a solution.  

Business objective: Design products to improve manufacturing.

• Adjusting product specifications to reduce manufacturing costs  

• Identified verifiable patterns of lost time during transitions from one job to the next and equipment set ups.
• Asked, are we designing products and processes that make it easy to produce? Where, specifically, can we look to streamline the setups? Do we need to upgrade machines or train people better?

Manufacturing engineer

Who he is: He is in charge of internal cost reductions and getting the most out of current machines, people and systems. He is at the intersection of people and processes and wants to wring as much out of every minute as possible. 

Business objective: Improving manufacturing efficiency

• Reducing unplanned downtime
• Improving manufacturing cycle times
• Minimizing scrap 

• Identified optimal times for preventative maintenance.
• Created a program where technicians “adopted” different machines, competed.
• Technicians shared information to better triage breakdowns and repairs.
• The downtime minutes and PM work is being collected as capital planning activity.
• A defunct cost of goods reduction team was revived with a new review structure focused on data and processes versus finger-pointing and emotion.
• Was able to craft laser-focused solutions to reduce 72 incidents of unplanned down time to less than 20 in their first effort.

Shop floor supervisor

Who he is: He knows the people and the machines — and how to pair them to get the best work. He wants his pairing magic on capabilities to go beyond his head so the entire team can leverage it. And he can find time to train his people versus solving problems himself.  

Business objective: Creating a productive, consistent workforce

• High levels of turnover
• Significant variation in employee skill levels
• Incentivizing and rewarding performance

• Helped design an operator performance view.
• Used data to determine which operators and work centers perform the best.
• Dug deeper to see if efficiencies were people-, process- or machine-based so he can replicate.
• Used data to determine which operators are most versatile and establish training programs.
• Operator 94 can run 13 different machines while maintaining a 96% performance and a high total unit count.
• Started sharing data at shift changes to reduce downtime.
• Establish alerts and alarms when downtime was extraordinary and required alerts and alarms.
• Used real-time analytics to have constructive conversations with operators on the floor respond events as they happen.

Get started with Machine Metrics

Interested in learning how to put the power of data into your decisions? Learn more on our IIoT solutions web page.